With a focus on high-throughput sequencing data
CFAES Bioinformatics Core, OSU
2025-08-26
Copyright ThermoFisher
Note
Should be large-scale, e.g. “genomics” is largely at the “whole-genome” level.
Both genomics and transcriptomics data, in the broad definitions above, is produced by high-throughput sequencing technologies.
That will be the focus of this lecture and will be used in examples throughout the course.
The shorthand sequencing, like in “high-throughput sequencing”, generally refers to determining the nucleotide sequence of fragments of DNA.
What about RNA or proteins?
RNA is usually reverse transcribed to DNA (cDNA) prior to sequencing, as in nearly all “RNA-seq”.
Direct RNA sequencing is possible with one of the sequencing technologies we’ll discuss, but this is under development and not yet widely used.
Sanger sequencing (since 1977)
Sequences a single, typically PCR-amplified, short-ish (≤900 bp) DNA fragment at a time
High-throughput sequencing (HTS, since 2005)
Sequences hundreds of thousand to billions, usually randomly selected, DNA fragments at a time
Sequenced DNA fragments are referred to as “reads”.
https://www.genome.gov/about-genomics/fact-sheets/Sequencing-Human-Genome-cost
Variant analysis (for population genetics/genomics, molecular evolution, GWAS, etc.):
Whole-genome “resequencing”
Reduced-representation libraries (e.g. RADseq, GBS)
Microbial community characterization
Metabarcoding
Shotgun metagenomics
Short-read HTS
Produces up to billions of 50-300 bp highly accurate reads
Market dominated by Illumina
Since 2005 — technology fairly stable
(AKA Next-Generation Sequencing - NGS)
Long-read HTS
Short videos explaining the technology (90 s - 5 m each)
Genome assembly
Haplotype and large structural variant calling
Transcript isoform identification
Taxonomic identification of single reads (microbial metabarcoding)
SNP variant analysis
Read-as-a-tag: the goal is just to know a read’s origin in a reference genome, like in counting applications such as RNA-seq
Currently, no sequencing technology is error-free.
Error rates are changing
Error rates in one recent type of PacBio sequencing where individual fragments are sequenced multiple times (“HiFi”) are now lower than in Illumina.
Error rates of ONT sequencing are also continuously decreasing.
Quality scores in sequence data
When you get sequences from a high-throughput sequencer, base calls have typically already been made. Every base is also accompanied by a quality score (inversely related to the estimated error probability). We’ll talk about those in some more detail in a bit.
We will talk a but about Illumina library prep because this is the most common type of sequencing, and because throughout the course, we will use Illumina read files as examples.
In a sequencing context, a “library” is a collection of nucleic acid fragments ready for sequencing.
In Illumina and other HTS libraries, these fragments number in the millions or billions and are often randomly generated from input such as genomic DNA:
An overview of the library prep procedure. This is typically done for you by a sequencing facility or company.
As shown in the previous slide, after library prep, each DNA fragment is flanked by several types of short sequences that together make up the “adapters”:
Multiplexing!
Using the indices/barcodes in adapters, up to 96 samples can be multiplexed into a single library.
DNA fragments can be sequenced from both ends as shown below —
this is called “paired-end” (PE) sequencing:
When sequencing is instead single-end (SE), no reverse read is produced:
The size of the DNA fragment can vary – both by design and because of limited precision in size selection. In some cases, it is:
Many HTS applications either require a “reference genome” or involve its production.
What exactly does “reference genome” refer to? It usually includes:
Assembly
A representation of most of the genome DNA sequence: the genome assembly
Annotation
The “annotation” that provides the locations of genes and other genomic features, as well as functional information on these features
Taxonomic identity
Reference genomes are typically needed and used at the species level.
All common seqeunce/genomic data files are plain-text. The main types are:
FASTQ
The standard format for HTS reads — contains a quality score for each nucleotide.
SAM/BAM
An alignment format for HTS reads.
FASTA files contain one or more DNA or amino acid sequences, with no limits on the number of sequences or the sequence lengths.
The following example FASTA file contains two entries:
>unique_sequence_ID Optional description
ATTCATTAAAGCAGTTTATTGGCTTAATGTACATCAGTGAAATCATAAATGCTAAAAA
>unique_sequence_ID2
ATTCATTAAAGCAGTTTATTGGCTTAATGTACATCAGTGAAATCATAAATGCTAAATGEach entry contains a header and the sequence itself, where:
> and provide identifying information for the sequenceFASTQ is the standard format for HTS reads.
Each read forms one FASTQ entry and is represented by four lines, which contain, respectively:
@ and e.g. uniquely identifies the read+ (plus sign)The quality scores we saw in the read on the previous slide represent an estimate of the error probability of the base call.
Specifically, they correspond to a numeric “Phred” quality score (Q), which is a function of the estimated probability that a base call is erroneous (P):
Q = -10 * log10(P)
For some specific probabilities and their rough qualitative interpretation for Illumina data:
| Phred quality score | Error probability | Rough interpretation |
|---|---|---|
| 10 | 1 in 10 | terrible |
| 20 | 1 in 100 | bad |
| 30 | 1 in 1,000 | good |
| 40 | 1 in 10,000 | excellent |
This numeric quality score is represented in FASTQ files not by the number itself, but by a corresponding “ASCII character”.
This allows for a single-character representation of each possible score — as a consequence, each quality score character can conveniently correspond to (& line up with) a base character in the read.
| Phred quality score | Error probability | ASCII character |
|---|---|---|
| 10 | 1 in 10 | + |
| 20 | 1 in 100 | 5 |
| 30 | 1 in 1,000 | ? |
| 40 | 1 in 10,000 | I |
A rule of thumb
In practice, you almost never have to manually check the quality scores of bases in FASTQ files, but if you do, a rule of thumb is that letter characters are good (Phred of 32 and up).
FASTQ files have no size limit, but in paired-end (PE) sequencing, forward and reverse reads are split into two files:
forward reads contain R1 and reverse reads contain R2 in the file name.
For example, having paired-end FASTQ files for 2 samples could look like this:
TBA
Modified after Pereira et al. 2020
Sequencing every bases multiple times, i.e. having a >1x so-called “depth of coverage” allows to infer the correct sequence: